How well must climate models agree with observations?
The usefulness of a climate-model simulation cannot be inferred solely from its degree of agreement with observations. Instead, one has to consider additional factors such as internal variability, the tuning of the model, observational uncertainty, the temporal change in dominant processes or the un...
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ftpubman:oai:pure.mpg.de:item_2196852 2023-08-20T04:09:43+02:00 How well must climate models agree with observations? Notz, D. 2015-10-13 http://hdl.handle.net/11858/00-001M-0000-0028-6588-5 eng eng info:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2014.0164 http://hdl.handle.net/11858/00-001M-0000-0028-6588-5 Philosophical Transactions of the Royal Society of London, Series A: Mathematical and Physical Sciences info:eu-repo/semantics/article 2015 ftpubman https://doi.org/10.1098/rsta.2014.0164 2023-08-01T23:05:21Z The usefulness of a climate-model simulation cannot be inferred solely from its degree of agreement with observations. Instead, one has to consider additional factors such as internal variability, the tuning of the model, observational uncertainty, the temporal change in dominant processes or the uncertainty in the forcing. In any model-evaluation study, the impact of these limiting factors on the suitability of specific metrics must hence be examined. This can only meaningfully be done relative to a given purpose for using amodel. I here generally discuss these points and substantiate their impact on model evaluation using the example of sea ice. For this example, I find that many standard metrics such as sea-ice area or volume only permit limited inferences about the shortcomings of individual models. © 2015 The Author(s) Published by the Royal Society. All rights reserved. Article in Journal/Newspaper Sea ice Max Planck Society: MPG.PuRe Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373 2052 20140164 |
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The usefulness of a climate-model simulation cannot be inferred solely from its degree of agreement with observations. Instead, one has to consider additional factors such as internal variability, the tuning of the model, observational uncertainty, the temporal change in dominant processes or the uncertainty in the forcing. In any model-evaluation study, the impact of these limiting factors on the suitability of specific metrics must hence be examined. This can only meaningfully be done relative to a given purpose for using amodel. I here generally discuss these points and substantiate their impact on model evaluation using the example of sea ice. For this example, I find that many standard metrics such as sea-ice area or volume only permit limited inferences about the shortcomings of individual models. © 2015 The Author(s) Published by the Royal Society. All rights reserved. |
format |
Article in Journal/Newspaper |
author |
Notz, D. |
spellingShingle |
Notz, D. How well must climate models agree with observations? |
author_facet |
Notz, D. |
author_sort |
Notz, D. |
title |
How well must climate models agree with observations? |
title_short |
How well must climate models agree with observations? |
title_full |
How well must climate models agree with observations? |
title_fullStr |
How well must climate models agree with observations? |
title_full_unstemmed |
How well must climate models agree with observations? |
title_sort |
how well must climate models agree with observations? |
publishDate |
2015 |
url |
http://hdl.handle.net/11858/00-001M-0000-0028-6588-5 |
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Sea ice |
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Sea ice |
op_source |
Philosophical Transactions of the Royal Society of London, Series A: Mathematical and Physical Sciences |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2014.0164 http://hdl.handle.net/11858/00-001M-0000-0028-6588-5 |
op_doi |
https://doi.org/10.1098/rsta.2014.0164 |
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Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |
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373 |
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2052 |
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20140164 |
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1774723356329771008 |